




"""simple docstring"""







import baseaa
import io
import json
import os
from copy import deepcopy

from ..optimizer import AcceleratedOptimizer
from ..scheduler import AcceleratedScheduler






class    SCREAMING_SNAKE_CASE__       :


						def __init__(				self					:      List[Any]    ,    lowerCAmelCase					:      str       ):
										if isinstance(__snake_case    ,    __snake_case       ):
														# Don't modify user's data should they want to reuse it (e.g. in tests), because once we
														# modified it, it will not be accepted here again, since `auto` values would have been overridden
														lowerCAmelCase						=					deepcopy(__snake_case       )
										elif os.path.exists(__snake_case       ):
														with io.open(__snake_case    ,    """r"""    ,    encoding="""utf-8"""       ) as f:
																		lowerCAmelCase						=					json.load(__snake_case       )
										else:
														try:
																		lowerCAmelCase						=					baseaa.urlsafe_baadecode(__snake_case       ).decode("""utf-8"""       )
																		lowerCAmelCase						=					json.loads(__snake_case       )
														except (UnicodeDecodeError, AttributeError, ValueError):
																		raise ValueError(
																		    f'''Expected a string path to an existing deepspeed config, or a dictionary, or a base64 encoded string. Received: {config_file_or_dict}'''       )

										lowerCAmelCase						=					config

										self.set_stage_and_offload()


						def 	__lowercase			(				self					:      Any       ):
										# zero stage - this is done as early as possible, before model is created, to allow
										# ``is_deepspeed_zero3_enabled`` query and getting to the early deepspeed config object
										# during ``zero.Init()`` which needs to know the dtype, and some other hparams.
										lowerCAmelCase						=					self.get_value("""zero_optimization.stage"""    ,    -1       )

										# offload
										lowerCAmelCase						=					False
										if self.is_zeroa() or self.is_zeroa():
														lowerCAmelCase						=					set(["""cpu""", """nvme"""]       )
														lowerCAmelCase						=					set(
														    [
														        self.get_value("""zero_optimization.offload_optimizer.device"""       ),
														        self.get_value("""zero_optimization.offload_param.device"""       ),
														    ]       )
														if len(offload_devices & offload_devices_valid       ) > 0:
																		lowerCAmelCase						=					True


						def 	__lowercase			(				self					:      str    ,    lowerCAmelCase					:      Optional[Any]       ):
										lowerCAmelCase						=					self.config

										# find the config node of interest if it exists
										lowerCAmelCase						=					ds_key_long.split("""."""       )
										lowerCAmelCase						=					nodes.pop()
										for node in nodes:
														lowerCAmelCase						=					config.get(__snake_case       )
														if config is None:
																		return None, ds_key

										return config, ds_key


						def 	__lowercase			(				self					:      int    ,    lowerCAmelCase					:      Optional[int]    ,    lowerCAmelCase					:      List[str]=None       ):
										lowerCAmelCase						=					self.find_config_node(__snake_case       )
										if config is None:
														return default
										return config.get(__snake_case    ,    __snake_case       )


						def 	__lowercase			(				self					:      Dict    ,    lowerCAmelCase					:      List[str]    ,    lowerCAmelCase					:      List[Any]=False       ):
										lowerCAmelCase						=					self.config

										# find the config node of interest if it exists
										lowerCAmelCase						=					ds_key_long.split("""."""       )
										for node in nodes:
														lowerCAmelCase						=					config
														lowerCAmelCase						=					config.get(__snake_case       )
														if config is None:
																		if must_exist:
																						raise ValueError(f'''Can\'t find {ds_key_long} entry in the config: {self.config}'''       )
																		else:
																						return

														# if found remove it
										if parent_config is not None:
														parent_config.pop(__snake_case       )


						def 	__lowercase			(				self					:      List[str]    ,    lowerCAmelCase					:      Dict       ):
										lowerCAmelCase						=					self.get_value(__snake_case       )
										return False if value is None else bool(__snake_case       )


						def 	__lowercase			(				self					:      str    ,    lowerCAmelCase					:      Optional[Any]       ):
										lowerCAmelCase						=					self.get_value(__snake_case       )
										return False if value is None else not bool(__snake_case       )


						def 	__lowercase			(				self					:      Tuple       ):
										return self._stage == 2


						def 	__lowercase			(				self					:      str       ):
										return self._stage == 3




						def 	__lowercase			(				self					:      int       ):
										return self._offload






class    SCREAMING_SNAKE_CASE__       :


						def __init__(				self					:      str    ,    lowerCAmelCase					:      Any       ):
										lowerCAmelCase						=					engine




						def 	__lowercase			(				self					:      Optional[Any]    ,    lowerCAmelCase					:      int    ,    **lowerCAmelCase					:      List[Any]       ):
										# runs backpropagation and handles mixed precision
										self.engine.backward(__snake_case    ,    **__snake_case       )

										# Deepspeed's `engine.step` performs the following operations:
										# - gradient accumulation check
										# - gradient clipping
										# - optimizer step
										# - zero grad
										# - checking overflow
										# - lr_scheduler step (only if engine.lr_scheduler is not None)
										self.engine.step()
										# and this plugin overrides the above calls with no-ops when Accelerate runs under
										# Deepspeed, but allows normal functionality for non-Deepspeed cases thus enabling a simple
										# training loop that works transparently under many training regimes.






class    SCREAMING_SNAKE_CASE__       (       lowerCamelCase__       ):


						def __init__(				self					:      Dict    ,    lowerCAmelCase					:      Any       ):
										super().__init__(__snake_case    ,    device_placement=__snake_case    ,    scaler=__snake_case       )
										lowerCAmelCase						=					hasattr(self.optimizer    ,    """overflow"""       )


						def 	__lowercase			(				self					:      Optional[Any]    ,    lowerCAmelCase					:      str=None       ):
										pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed


						def 	__lowercase			(				self					:      int       ):
										pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed




						@property
						def 	__lowercase			(				self					:      Any       ):
										if self.__has_overflow__:
														return self.optimizer.overflow
										return False






class    SCREAMING_SNAKE_CASE__       (       lowerCamelCase__       ):


						def __init__(				self					:      str    ,    lowerCAmelCase					:      List[Any]    ,    lowerCAmelCase					:      str       ):
										super().__init__(__snake_case    ,    __snake_case       )




						def 	__lowercase			(				self					:      Union[str, Any]       ):
										pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed






class    SCREAMING_SNAKE_CASE__       :


						def __init__(				self					:      Optional[Any]    ,    lowerCAmelCase					:      Union[str, Any]    ,    lowerCAmelCase					:      int=0.001    ,    lowerCAmelCase					:      Any=0    ,    **lowerCAmelCase					:      List[Any]       ):
										lowerCAmelCase						=					params
										lowerCAmelCase						=					lr
										lowerCAmelCase						=					weight_decay
										lowerCAmelCase						=					kwargs






class    SCREAMING_SNAKE_CASE__       :


						def __init__(				self					:      Any    ,    lowerCAmelCase					:      int    ,    lowerCAmelCase					:      int=None    ,    lowerCAmelCase					:      Dict=0    ,    **lowerCAmelCase					:      Optional[int]       ):
										lowerCAmelCase						=					optimizer
										lowerCAmelCase						=					total_num_steps
										lowerCAmelCase						=					warmup_num_steps
										lowerCAmelCase						=					kwargs










'''simple docstring'''






import os

try:
     from .build_directory_md import good_file_paths
except ImportError:
     from build_directory_md import good_file_paths  # type: ignore

lowerCAmelCase:   Optional[int]					=		list(good_file_paths())
assert filepaths, "good_file_paths() failed!"

lowerCAmelCase:   Optional[Any]					=		[file for file in filepaths if file != file.lower()]
if upper_files:
     print(F"{len(upper_files)} files contain uppercase characters:")
     print('\n'.join(upper_files) + '\n')

lowerCAmelCase:   int					=		[file for file in filepaths if ' ' in file]
if space_files:
     print(F"{len(space_files)} files contain space characters:")
     print('\n'.join(space_files) + '\n')

lowerCAmelCase:   Union[str, Any]					=		[file for file in filepaths if '-' in file]
if hyphen_files:
     print(F"{len(hyphen_files)} files contain hyphen characters:")
     print('\n'.join(hyphen_files) + '\n')

lowerCAmelCase:   Union[str, Any]					=		[file for file in filepaths if os.sep not in file]
if nodir_files:
     print(F"{len(nodir_files)} files are not in a directory:")
     print('\n'.join(nodir_files) + '\n')

lowerCAmelCase:   int					=		len(upper_files + space_files + hyphen_files + nodir_files)
if bad_files:
     import sys

     sys.exit(bad_files)